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This study addresses an issue of co-adaptation between a feature extractor and a classifier in a neural network.
Bagging predictors
Breiman, L · 1996
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Algebraic geometry and statistical learning theory
Watanabe, S · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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Practical variational inference for neural networks
Graves, A · 2011
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2012
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Understanding dropout
Baldi, P. and Sadowski, P. J · 2013
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A. C · 2013
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Dropout training as adaptive regularization
Wager, S., Wang, S., and Liang, P. S · 2013
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Regularization of neural network using dropconnect
Wan, L., Zeiler, M., Zhang, S., LeCun, Y., and Fergus, R · 2013
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An empirical analysis of dropout in piecewise linear networks
Warde-Farley, D., Goodfellow, I. J., Courville, A. C., and Bengio, Y · 2013
Cited alongside, same era.
Stochastic pooling for regularization of deep convolutional neural networks
Zeiler, M. and Fergus, R · 2013
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
Cited alongside, same era.
On the inductive bias of dropout
Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q · 2016
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Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
Lee, C.-Y., Gallagher, P., and Tu, Z · 2016
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Ensemble classification and regression-recent developments, applications and future directions [review article]
Ren, Y., Zhang, L., and Suganthan, P. N · 2016
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Swapout: Learning an ensemble of deep architectures
Singh, S., Hoiem, D., and Forsyth, D · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Helmbold, D. P. and Long, P. M · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Cited alongside, same era.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Hara, K., Saitoh, D., and Shouno, H · 2017
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Sharing convnet across heterogeneous tasks
Kobayashi, T · 2017
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Surprising properties of dropout in deep networks
Helmbold, D. and Long, P · 2018
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Ensemble robustness and generalization of stochastic learning algorithms
Zahavy, T., Sivak, A., Kang, B., Feng, J., and Mannor, H. X. S · 2018
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